DOI: 10.1002/2017MS000942
Scopus记录号: 2-s2.0-85031897189
论文题名: Multiobjective constraints for climate model parameter choices: Pragmatic Pareto fronts in CESM1
作者: Langenbrunner B ; , Neelin J ; D
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2017
卷: 9, 期: 5 起始页码: 2008
结束页码: 2026
语种: 英语
英文关键词: Commerce
; Economic and social effects
; Multiobjective optimization
; Pareto principle
; Tropics
; CESM1
; Global climate model
; High dimensional model representation
; Meta-modeling technique
; Parameter optimization
; Parameter sensitivities
; Seasonal precipitations
; tropical pacific climate
; Climate models
; algorithm
; climate modeling
; model test
; multiobjective programming
; optimization
; parameterization
; precipitation (climatology)
; regional climate
; statistical analysis
; temperature profile
; visualization
; water vapor
; Pacific Ocean
; Pacific Ocean (Tropical)
英文摘要: Global climate models (GCMs) are examples of high-dimensional input-output systems, where model output is a function of many variables, and an update in model physics commonly improves performance in one objective function (i.e., measure of model performance) at the expense of degrading another. Here concepts from multiobjective optimization in the engineering literature are used to investigate parameter sensitivity and optimization in the face of such trade-offs. A metamodeling technique called cut high-dimensional model representation (cut-HDMR) is leveraged in the context of multiobjective optimization to improve GCM simulation of the tropical Pacific climate, focusing on seasonal precipitation, column water vapor, and skin temperature. An evolutionary algorithm is used to solve for Pareto fronts, which are surfaces in objective function space along which trade-offs in GCM performance occur. This approach allows the modeler to visualize trade-offs quickly and identify the physics at play. In some cases, Pareto fronts are small, implying that trade-offs are minimal, optimal parameter value choices are more straightforward, and the GCM is well-functioning. In all cases considered here, the control run was found not to be Pareto-optimal (i.e., not on the front), highlighting an opportunity for model improvement through objectively informed parameter selection. Taylor diagrams illustrate that these improvements occur primarily in field magnitude, not spatial correlation, and they show that specific parameter updates can improve fields fundamental to tropical moist processes—namely precipitation and skin temperature—without significantly impacting others. These results provide an example of how basic elements of multiobjective optimization can facilitate pragmatic GCM tuning processes. © 2017. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75728
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, United States
Recommended Citation:
Langenbrunner B,, Neelin J,D. Multiobjective constraints for climate model parameter choices: Pragmatic Pareto fronts in CESM1[J]. Journal of Advances in Modeling Earth Systems,2017-01-01,9(5)